Identification of Networks of Sexually Transmitted Infection: A Molecular, Geographic, and Social Network Analysis
Bibliographic record
Abstract
BACKGROUND: Despite widespread efforts to control it, Chlamydia trachomatis remains the most frequently diagnosed bacterial sexually transmitted infection (STI). Analysis of sexual networks has been proposed as a novel tool for control of and research into STI. In the present study, we combine molecular genotype data, analysis of geographic clusters, and sociodemographic descriptors to facilitate analysis of large sexual networks. METHODS: Individual chlamydia genotypes found in Manitoba, Canada, were analyzed to identify geographic clusters, and the identified clusters were further characterized by statistical analysis of sociodemographic variables. RESULTS: A total of 10 geographic clusters of chlamydia-genotype infection were identified. Clusters in Winnipeg showed no or little geographic overlap and could be further differentiated on the basis of the sociodemographic characteristics of the individuals within a cluster. Several clusters in northern Manitoba overlapped geographically but, nonetheless, could be differentiated on the basis of the sociodemographic characteristics of the infected individuals. CONCLUSIONS: On the basis of results of the combined analyses, each geographic cluster appeared to represent a relatively distinct transmission network within the larger sexual network. The geographic analysis of the molecular data provided a basis for establishment of potential epidemiological connections between small groups of unlinked individuals. Analytic approaches of the type described here would help to decipher the patterns that exist within large social network data sets and would be applicable to many types of infectious agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".